Question

我已经将二进制分类器定义为风箱：我用'gbc'方法（Gradient Boosting Classifier）调用它，我在最后一行得到错误min_samples_split must be at least 2 or in (0, 1], got 1。 featuresClasses是一个数据框，featureLabels是功能列表。

Binary_classifier(method, featureLabels, featuresClasses):

    membershipIds = list(set(featuresClasses['membershipId']))
    n_membershipIds = len(membershipIds)

    index_rand = np.random.permutation(n_membershipIds)
    test_size = int(0.3 * n_membershipIds)

    membershipIds_test = list(itemgetter(*index_rand[:test_size])(membershipIds))
    membershipIds_train = list(itemgetter(*index_rand[test_size+1:])(membershipIds))

    data_test = featuresClasses[featuresClasses['membershipId'].isin(membershipIds_test)]
    data_train = featuresClasses[featuresClasses['membershipId'].isin(membershipIds_train)]

    data_test = data_test[data_test['standing'].isin([0, 1])]
    data_train = data_train[data_train['standing'].isin([0, 1])]

    X_test = data_test[featureLabels].as_matrix()
    y_test = data_test['standing'].values.astype(int)

    X_train = data_train[featureLabels].as_matrix()
    y_train = data_train['standing'].values.astype(int)

    # -------------------------- Run classifier
    print 'Binary classification by', method

    if method == 'svm':
        classifier = svm.SVC(kernel='linear', probability=True)
        y_score = classifier.fit(X_train, y_train).decision_function(X_test)

    elif method == 'gbc':
        params = {'n_estimators': 200, 'max_depth': 3, 'min_samples_split': 1, 'learning_rate': 0.1, 'loss': 'deviance'}

        classifier = GradientBoostingClassifier(**params)
        y_score = classifier.fit(X_train, y_train).predict(X_test)

Answer 1

根据GradientBoostingClassifier documentation：

min_samples_split：int，float，optional（default = 2）

The minimum number of samples required to split an internal node:

    If int, then consider min_samples_split as the minimum number.
    If float, then min_samples_split is a percentage and ceil(min_samples_split * n_samples) 
               are the minimum number of samples for each split.

您在代码中指定了'min_samples_split': 1。这不是一个有效的案例。它的最小int值是2。如果你想输入1作为浮点数（即1 *个特征数）（即你想将所有特征都带入min_samples_split），那么指定为'min_samples_split': 1.0。如果指定为1，则将其视为int，因此会发生错误。

这是错误的差异，显示为（0,1）而不是（0.0,1.0），这引起了混淆。这也被问及关于scikit-learn的github问题，并已在下一步实施释放：

https://github.com/scikit-learn/scikit-learn/issues/8041

min_samples_split必须至少为2或在（0,1）中，得1

1 个答案: